Overview

Brought to you by YData

Dataset statistics

Number of variables29
Number of observations2016
Missing cells23
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory747.4 KiB
Average record size in memory379.7 B

Variable types

Numeric15
Categorical13
DateTime1

Alerts

Contact_Cost has constant value "3" Constant
Total_Revenue has constant value "11" Constant
Annual_Income is highly overall correlated with Catalog_Orders and 9 other fieldsHigh correlation
Campaign_3 is highly overall correlated with Spent_WinesHigh correlation
Catalog_Orders is highly overall correlated with Annual_Income and 9 other fieldsHigh correlation
Spent_Fish is highly overall correlated with Annual_Income and 7 other fieldsHigh correlation
Spent_Fruits is highly overall correlated with Annual_Income and 7 other fieldsHigh correlation
Spent_Gold is highly overall correlated with Annual_Income and 8 other fieldsHigh correlation
Spent_Meat is highly overall correlated with Annual_Income and 8 other fieldsHigh correlation
Spent_Sweets is highly overall correlated with Annual_Income and 7 other fieldsHigh correlation
Spent_Wines is highly overall correlated with Annual_Income and 9 other fieldsHigh correlation
Store_Orders is highly overall correlated with Annual_Income and 8 other fieldsHigh correlation
Web_Orders is highly overall correlated with Annual_Income and 5 other fieldsHigh correlation
Web_Visits is highly overall correlated with Annual_Income and 1 other fieldsHigh correlation
Campaign_1 is highly imbalanced (62.9%) Imbalance
Campaign_2 is highly imbalanced (61.8%) Imbalance
Campaign_3 is highly imbalanced (62.7%) Imbalance
Campaign_4 is highly imbalanced (65.7%) Imbalance
Campaign_5 is highly imbalanced (89.7%) Imbalance
Complaint_Flag is highly imbalanced (92.3%) Imbalance
Annual_Income has 23 (1.1%) missing values Missing
User_Key has unique values Unique
Last_Visit has 22 (1.1%) zeros Zeros
Spent_Fruits has 361 (17.9%) zeros Zeros
Spent_Fish has 347 (17.2%) zeros Zeros
Spent_Sweets has 376 (18.7%) zeros Zeros
Spent_Gold has 56 (2.8%) zeros Zeros
Promo_Purchases has 43 (2.1%) zeros Zeros
Web_Orders has 47 (2.3%) zeros Zeros
Catalog_Orders has 524 (26.0%) zeros Zeros

Reproduction

Analysis started2025-07-14 15:49:00.320656
Analysis finished2025-07-14 15:50:14.485213
Duration1 minute and 14.16 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

User_Key
Real number (ℝ)

Unique 

Distinct2016
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5547.3606
Minimum0
Maximum11191
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2025-07-14T20:50:14.691590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile530.25
Q12745.25
median5454.5
Q38373.5
95-th percentile10653.75
Maximum11191
Range11191
Interquartile range (IQR)5628.25

Descriptive statistics

Standard deviation3252.987
Coefficient of variation (CV)0.58640266
Kurtosis-1.193306
Mean5547.3606
Median Absolute Deviation (MAD)2806.5
Skewness0.041045525
Sum11183479
Variance10581925
MonotonicityNot monotonic
2025-07-14T20:50:14.978195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10814 1
 
< 0.1%
5748 1
 
< 0.1%
7349 1
 
< 0.1%
2795 1
 
< 0.1%
800 1
 
< 0.1%
310 1
 
< 0.1%
9790 1
 
< 0.1%
5336 1
 
< 0.1%
3799 1
 
< 0.1%
2478 1
 
< 0.1%
Other values (2006) 2006
99.5%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
9 1
< 0.1%
13 1
< 0.1%
17 1
< 0.1%
20 1
< 0.1%
22 1
< 0.1%
24 1
< 0.1%
25 1
< 0.1%
35 1
< 0.1%
ValueCountFrequency (%)
11191 1
< 0.1%
11188 1
< 0.1%
11187 1
< 0.1%
11181 1
< 0.1%
11178 1
< 0.1%
11171 1
< 0.1%
11166 1
< 0.1%
11148 1
< 0.1%
11133 1
< 0.1%
11121 1
< 0.1%

Birth_Year
Real number (ℝ)

Distinct59
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1968.8289
Minimum1893
Maximum1996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2025-07-14T20:50:15.227532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1893
5-th percentile1950
Q11959
median1970
Q31977
95-th percentile1988
Maximum1996
Range103
Interquartile range (IQR)18

Descriptive statistics

Standard deviation11.999503
Coefficient of variation (CV)0.0060947414
Kurtosis0.87794148
Mean1968.8289
Median Absolute Deviation (MAD)9
Skewness-0.38163152
Sum3969159
Variance143.98807
MonotonicityNot monotonic
2025-07-14T20:50:15.848637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1976 82
 
4.1%
1975 77
 
3.8%
1972 72
 
3.6%
1978 71
 
3.5%
1965 70
 
3.5%
1971 70
 
3.5%
1970 69
 
3.4%
1973 67
 
3.3%
1974 61
 
3.0%
1969 61
 
3.0%
Other values (49) 1316
65.3%
ValueCountFrequency (%)
1893 1
 
< 0.1%
1899 1
 
< 0.1%
1900 1
 
< 0.1%
1940 1
 
< 0.1%
1941 1
 
< 0.1%
1943 6
 
0.3%
1944 7
0.3%
1945 6
 
0.3%
1946 14
0.7%
1947 16
0.8%
ValueCountFrequency (%)
1996 2
 
0.1%
1995 3
 
0.1%
1994 3
 
0.1%
1993 4
 
0.2%
1992 13
0.6%
1991 13
0.6%
1990 16
0.8%
1989 28
1.4%
1988 25
1.2%
1987 23
1.1%

Edu_Level
Categorical

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size111.4 KiB
Graduation
1012 
PhD
437 
Master
337 
2n Cycle
183 
Basic
 
47

Length

Max length10
Median length10
Mean length7.515873
Min length3

Characters and Unicode

Total characters15152
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGraduation
2nd rowGraduation
3rd rowBasic
4th rowGraduation
5th rowGraduation

Common Values

ValueCountFrequency (%)
Graduation 1012
50.2%
PhD 437
21.7%
Master 337
 
16.7%
2n Cycle 183
 
9.1%
Basic 47
 
2.3%

Length

2025-07-14T20:50:16.140894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-14T20:50:16.277551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
graduation 1012
46.0%
phd 437
19.9%
master 337
 
15.3%
2n 183
 
8.3%
cycle 183
 
8.3%
basic 47
 
2.1%

Most occurring characters

ValueCountFrequency (%)
a 2408
15.9%
r 1349
8.9%
t 1349
8.9%
n 1195
 
7.9%
i 1059
 
7.0%
G 1012
 
6.7%
u 1012
 
6.7%
d 1012
 
6.7%
o 1012
 
6.7%
e 520
 
3.4%
Other values (12) 3224
21.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15152
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2408
15.9%
r 1349
8.9%
t 1349
8.9%
n 1195
 
7.9%
i 1059
 
7.0%
G 1012
 
6.7%
u 1012
 
6.7%
d 1012
 
6.7%
o 1012
 
6.7%
e 520
 
3.4%
Other values (12) 3224
21.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15152
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2408
15.9%
r 1349
8.9%
t 1349
8.9%
n 1195
 
7.9%
i 1059
 
7.0%
G 1012
 
6.7%
u 1012
 
6.7%
d 1012
 
6.7%
o 1012
 
6.7%
e 520
 
3.4%
Other values (12) 3224
21.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15152
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2408
15.9%
r 1349
8.9%
t 1349
8.9%
n 1195
 
7.9%
i 1059
 
7.0%
G 1012
 
6.7%
u 1012
 
6.7%
d 1012
 
6.7%
o 1012
 
6.7%
e 520
 
3.4%
Other values (12) 3224
21.3%

Family_Status
Categorical

Distinct8
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size110.5 KiB
Married
780 
Together
519 
Single
427 
Divorced
210 
Widow
 
73
Other values (3)
 
7

Length

Max length8
Median length7
Mean length7.0704365
Min length4

Characters and Unicode

Total characters14254
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle
2nd rowWidow
3rd rowMarried
4th rowTogether
5th rowTogether

Common Values

ValueCountFrequency (%)
Married 780
38.7%
Together 519
25.7%
Single 427
21.2%
Divorced 210
 
10.4%
Widow 73
 
3.6%
Alone 3
 
0.1%
Absurd 2
 
0.1%
YOLO 2
 
0.1%

Length

2025-07-14T20:50:16.518972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-14T20:50:16.840226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
married 780
38.7%
together 519
25.7%
single 427
21.2%
divorced 210
 
10.4%
widow 73
 
3.6%
alone 3
 
0.1%
absurd 2
 
0.1%
yolo 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 2458
17.2%
r 2291
16.1%
i 1490
10.5%
d 1065
7.5%
g 946
 
6.6%
o 805
 
5.6%
a 780
 
5.5%
M 780
 
5.5%
T 519
 
3.6%
t 519
 
3.6%
Other values (16) 2601
18.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14254
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2458
17.2%
r 2291
16.1%
i 1490
10.5%
d 1065
7.5%
g 946
 
6.6%
o 805
 
5.6%
a 780
 
5.5%
M 780
 
5.5%
T 519
 
3.6%
t 519
 
3.6%
Other values (16) 2601
18.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14254
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2458
17.2%
r 2291
16.1%
i 1490
10.5%
d 1065
7.5%
g 946
 
6.6%
o 805
 
5.6%
a 780
 
5.5%
M 780
 
5.5%
T 519
 
3.6%
t 519
 
3.6%
Other values (16) 2601
18.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14254
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2458
17.2%
r 2291
16.1%
i 1490
10.5%
d 1065
7.5%
g 946
 
6.6%
o 805
 
5.6%
a 780
 
5.5%
M 780
 
5.5%
T 519
 
3.6%
t 519
 
3.6%
Other values (16) 2601
18.2%

Annual_Income
Real number (ℝ)

High correlation  Missing 

Distinct1794
Distinct (%)90.0%
Missing23
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean52460.201
Minimum1730
Maximum666666
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2025-07-14T20:50:17.131505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1730
5-th percentile18958.4
Q135688
median51479
Q368655
95-th percentile84205.2
Maximum666666
Range664936
Interquartile range (IQR)32967

Descriptive statistics

Standard deviation25596.195
Coefficient of variation (CV)0.48791646
Kurtosis165.88508
Mean52460.201
Median Absolute Deviation (MAD)16511
Skewness7.1441175
Sum1.0455318 × 108
Variance6.5516522 × 108
MonotonicityNot monotonic
2025-07-14T20:50:17.435047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7500 11
 
0.5%
46098 3
 
0.1%
37760 3
 
0.1%
67445 3
 
0.1%
18929 3
 
0.1%
47025 3
 
0.1%
34176 3
 
0.1%
83844 3
 
0.1%
18690 3
 
0.1%
35860 3
 
0.1%
Other values (1784) 1955
97.0%
(Missing) 23
 
1.1%
ValueCountFrequency (%)
1730 1
 
< 0.1%
3502 1
 
< 0.1%
4023 1
 
< 0.1%
4428 1
 
< 0.1%
4861 1
 
< 0.1%
5305 1
 
< 0.1%
5648 1
 
< 0.1%
6560 1
 
< 0.1%
6835 1
 
< 0.1%
7500 11
0.5%
ValueCountFrequency (%)
666666 1
< 0.1%
162397 1
< 0.1%
160803 1
< 0.1%
157733 1
< 0.1%
157243 1
< 0.1%
157146 1
< 0.1%
156924 1
< 0.1%
153924 1
< 0.1%
113734 1
< 0.1%
102692 1
< 0.1%

Kids_Count
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size98.6 KiB
0
1170 
1
802 
2
 
44

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2016
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1170
58.0%
1 802
39.8%
2 44
 
2.2%

Length

2025-07-14T20:50:17.671546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-14T20:50:17.849864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1170
58.0%
1 802
39.8%
2 44
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 1170
58.0%
1 802
39.8%
2 44
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2016
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1170
58.0%
1 802
39.8%
2 44
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2016
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1170
58.0%
1 802
39.8%
2 44
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2016
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1170
58.0%
1 802
39.8%
2 44
 
2.2%

Teens_Count
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size98.6 KiB
0
1036 
1
933 
2
 
47

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2016
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row2

Common Values

ValueCountFrequency (%)
0 1036
51.4%
1 933
46.3%
2 47
 
2.3%

Length

2025-07-14T20:50:18.106029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-14T20:50:18.227169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1036
51.4%
1 933
46.3%
2 47
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 1036
51.4%
1 933
46.3%
2 47
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2016
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1036
51.4%
1 933
46.3%
2 47
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2016
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1036
51.4%
1 933
46.3%
2 47
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2016
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1036
51.4%
1 933
46.3%
2 47
 
2.3%
Distinct654
Distinct (%)32.4%
Missing0
Missing (%)0.0%
Memory size15.9 KiB
Minimum2012-01-08 00:00:00
Maximum2014-12-06 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-14T20:50:18.460237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:50:18.759754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Last_Visit
Real number (ℝ)

Zeros 

Distinct100
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.33879
Minimum0
Maximum99
Zeros22
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2025-07-14T20:50:19.126629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q124.75
median50
Q374
95-th percentile94
Maximum99
Range99
Interquartile range (IQR)49.25

Descriptive statistics

Standard deviation28.974855
Coefficient of variation (CV)0.58726319
Kurtosis-1.1985839
Mean49.33879
Median Absolute Deviation (MAD)25
Skewness-0.013499736
Sum99467
Variance839.54224
MonotonicityNot monotonic
2025-07-14T20:50:19.454705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56 34
 
1.7%
46 29
 
1.4%
54 28
 
1.4%
30 27
 
1.3%
3 27
 
1.3%
71 27
 
1.3%
48 27
 
1.3%
24 27
 
1.3%
2 27
 
1.3%
51 26
 
1.3%
Other values (90) 1737
86.2%
ValueCountFrequency (%)
0 22
1.1%
1 22
1.1%
2 27
1.3%
3 27
1.3%
4 26
1.3%
5 13
0.6%
6 20
1.0%
7 11
0.5%
8 22
1.1%
9 22
1.1%
ValueCountFrequency (%)
99 16
0.8%
98 21
1.0%
97 17
0.8%
96 22
1.1%
95 18
0.9%
94 23
1.1%
93 21
1.0%
92 25
1.2%
91 18
0.9%
90 19
0.9%

Spent_Wines
Real number (ℝ)

High correlation 

Distinct745
Distinct (%)37.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean307.78224
Minimum0
Maximum1493
Zeros12
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2025-07-14T20:50:19.717358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q124
median180.5
Q3509
95-th percentile1000
Maximum1493
Range1493
Interquartile range (IQR)485

Descriptive statistics

Standard deviation338.21036
Coefficient of variation (CV)1.0988625
Kurtosis0.55017365
Mean307.78224
Median Absolute Deviation (MAD)170.5
Skewness1.1542065
Sum620489
Variance114386.24
MonotonicityNot monotonic
2025-07-14T20:50:20.009218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 38
 
1.9%
6 35
 
1.7%
1 33
 
1.6%
5 32
 
1.6%
4 29
 
1.4%
3 29
 
1.4%
9 26
 
1.3%
8 26
 
1.3%
12 24
 
1.2%
10 22
 
1.1%
Other values (735) 1722
85.4%
ValueCountFrequency (%)
0 12
 
0.6%
1 33
1.6%
2 38
1.9%
3 29
1.4%
4 29
1.4%
5 32
1.6%
6 35
1.7%
7 20
1.0%
8 26
1.3%
9 26
1.3%
ValueCountFrequency (%)
1493 1
< 0.1%
1492 2
0.1%
1486 1
< 0.1%
1478 2
0.1%
1462 1
< 0.1%
1459 1
< 0.1%
1449 1
< 0.1%
1396 1
< 0.1%
1394 1
< 0.1%
1379 1
< 0.1%

Spent_Fruits
Real number (ℝ)

High correlation  Zeros 

Distinct154
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.379464
Minimum0
Maximum199
Zeros361
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2025-07-14T20:50:20.395969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median8
Q333
95-th percentile122.25
Maximum199
Range199
Interquartile range (IQR)31

Descriptive statistics

Standard deviation39.808018
Coefficient of variation (CV)1.5090533
Kurtosis4.1146697
Mean26.379464
Median Absolute Deviation (MAD)8
Skewness2.1088263
Sum53181
Variance1584.6783
MonotonicityNot monotonic
2025-07-14T20:50:20.750706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 361
 
17.9%
1 142
 
7.0%
2 107
 
5.3%
3 103
 
5.1%
4 89
 
4.4%
7 65
 
3.2%
5 62
 
3.1%
6 56
 
2.8%
12 46
 
2.3%
8 44
 
2.2%
Other values (144) 941
46.7%
ValueCountFrequency (%)
0 361
17.9%
1 142
 
7.0%
2 107
 
5.3%
3 103
 
5.1%
4 89
 
4.4%
5 62
 
3.1%
6 56
 
2.8%
7 65
 
3.2%
8 44
 
2.2%
9 29
 
1.4%
ValueCountFrequency (%)
199 2
0.1%
197 1
 
< 0.1%
194 3
0.1%
193 2
0.1%
190 1
 
< 0.1%
189 1
 
< 0.1%
185 2
0.1%
184 1
 
< 0.1%
183 3
0.1%
178 3
0.1%

Spent_Meat
Real number (ℝ)

High correlation 

Distinct533
Distinct (%)26.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean168.10516
Minimum0
Maximum1725
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2025-07-14T20:50:21.041339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q116
median68
Q3232.25
95-th percentile689
Maximum1725
Range1725
Interquartile range (IQR)216.25

Descriptive statistics

Standard deviation225.21616
Coefficient of variation (CV)1.3397338
Kurtosis4.9935144
Mean168.10516
Median Absolute Deviation (MAD)60
Skewness2.0214
Sum338900
Variance50722.318
MonotonicityNot monotonic
2025-07-14T20:50:21.323894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 49
 
2.4%
5 48
 
2.4%
11 42
 
2.1%
8 41
 
2.0%
6 36
 
1.8%
10 35
 
1.7%
16 34
 
1.7%
3 34
 
1.7%
9 33
 
1.6%
12 30
 
1.5%
Other values (523) 1634
81.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 14
 
0.7%
2 25
1.2%
3 34
1.7%
4 28
1.4%
5 48
2.4%
6 36
1.8%
7 49
2.4%
8 41
2.0%
9 33
1.6%
ValueCountFrequency (%)
1725 1
< 0.1%
1622 1
< 0.1%
1607 1
< 0.1%
1582 1
< 0.1%
984 1
< 0.1%
981 1
< 0.1%
974 1
< 0.1%
968 1
< 0.1%
961 1
< 0.1%
951 2
0.1%

Spent_Fish
Real number (ℝ)

High correlation  Zeros 

Distinct180
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.573413
Minimum0
Maximum259
Zeros347
Zeros (%)17.2%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2025-07-14T20:50:21.673822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median12
Q350
95-th percentile168.25
Maximum259
Range259
Interquartile range (IQR)47

Descriptive statistics

Standard deviation54.532714
Coefficient of variation (CV)1.4513644
Kurtosis3.0864928
Mean37.573413
Median Absolute Deviation (MAD)12
Skewness1.9137956
Sum75748
Variance2973.8169
MonotonicityNot monotonic
2025-07-14T20:50:21.987029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 347
 
17.2%
2 141
 
7.0%
3 114
 
5.7%
4 96
 
4.8%
6 72
 
3.6%
7 63
 
3.1%
8 53
 
2.6%
10 49
 
2.4%
13 46
 
2.3%
12 43
 
2.1%
Other values (170) 992
49.2%
ValueCountFrequency (%)
0 347
17.2%
1 9
 
0.4%
2 141
7.0%
3 114
 
5.7%
4 96
 
4.8%
6 72
 
3.6%
7 63
 
3.1%
8 53
 
2.6%
10 49
 
2.4%
11 41
 
2.0%
ValueCountFrequency (%)
259 1
 
< 0.1%
258 3
0.1%
254 1
 
< 0.1%
253 1
 
< 0.1%
250 3
0.1%
246 1
 
< 0.1%
242 1
 
< 0.1%
240 1
 
< 0.1%
237 2
0.1%
234 2
0.1%

Spent_Sweets
Real number (ℝ)

High correlation  Zeros 

Distinct175
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.103671
Minimum0
Maximum263
Zeros376
Zeros (%)18.7%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2025-07-14T20:50:22.487777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8
Q333
95-th percentile128.25
Maximum263
Range263
Interquartile range (IQR)32

Descriptive statistics

Standard deviation41.34326
Coefficient of variation (CV)1.525375
Kurtosis4.5369814
Mean27.103671
Median Absolute Deviation (MAD)8
Skewness2.163778
Sum54641
Variance1709.2652
MonotonicityNot monotonic
2025-07-14T20:50:22.806015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 376
 
18.7%
1 140
 
6.9%
2 109
 
5.4%
3 95
 
4.7%
4 73
 
3.6%
5 61
 
3.0%
6 58
 
2.9%
8 53
 
2.6%
7 49
 
2.4%
12 41
 
2.0%
Other values (165) 961
47.7%
ValueCountFrequency (%)
0 376
18.7%
1 140
 
6.9%
2 109
 
5.4%
3 95
 
4.7%
4 73
 
3.6%
5 61
 
3.0%
6 58
 
2.9%
7 49
 
2.4%
8 53
 
2.6%
9 39
 
1.9%
ValueCountFrequency (%)
263 1
 
< 0.1%
262 1
 
< 0.1%
198 1
 
< 0.1%
197 1
 
< 0.1%
196 1
 
< 0.1%
195 1
 
< 0.1%
194 2
0.1%
192 3
0.1%
191 1
 
< 0.1%
189 2
0.1%

Spent_Gold
Real number (ℝ)

High correlation  Zeros 

Distinct209
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.200397
Minimum0
Maximum362
Zeros56
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2025-07-14T20:50:23.160831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q19
median24
Q357
95-th percentile168
Maximum362
Range362
Interquartile range (IQR)48

Descriptive statistics

Standard deviation52.310692
Coefficient of variation (CV)1.1834892
Kurtosis3.3544692
Mean44.200397
Median Absolute Deviation (MAD)18
Skewness1.8517241
Sum89108
Variance2736.4085
MonotonicityNot monotonic
2025-07-14T20:50:23.591228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 66
 
3.3%
1 61
 
3.0%
5 60
 
3.0%
12 57
 
2.8%
0 56
 
2.8%
2 56
 
2.8%
4 56
 
2.8%
6 55
 
2.7%
10 48
 
2.4%
7 46
 
2.3%
Other values (199) 1455
72.2%
ValueCountFrequency (%)
0 56
2.8%
1 61
3.0%
2 56
2.8%
3 66
3.3%
4 56
2.8%
5 60
3.0%
6 55
2.7%
7 46
2.3%
8 37
1.8%
9 37
1.8%
ValueCountFrequency (%)
362 1
 
< 0.1%
321 1
 
< 0.1%
262 1
 
< 0.1%
249 1
 
< 0.1%
248 1
 
< 0.1%
247 1
 
< 0.1%
246 1
 
< 0.1%
245 1
 
< 0.1%
242 1
 
< 0.1%
241 5
0.2%

Promo_Purchases
Real number (ℝ)

Zeros 

Distinct15
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3343254
Minimum0
Maximum15
Zeros43
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2025-07-14T20:50:23.972234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile6
Maximum15
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9283267
Coefficient of variation (CV)0.82607452
Kurtosis8.6317655
Mean2.3343254
Median Absolute Deviation (MAD)1
Skewness2.3699437
Sum4706
Variance3.718444
MonotonicityNot monotonic
2025-07-14T20:50:24.213227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 864
42.9%
2 446
22.1%
3 273
 
13.5%
4 172
 
8.5%
5 83
 
4.1%
6 56
 
2.8%
0 43
 
2.1%
7 37
 
1.8%
8 14
 
0.7%
9 8
 
0.4%
Other values (5) 20
 
1.0%
ValueCountFrequency (%)
0 43
 
2.1%
1 864
42.9%
2 446
22.1%
3 273
 
13.5%
4 172
 
8.5%
5 83
 
4.1%
6 56
 
2.8%
7 37
 
1.8%
8 14
 
0.7%
9 8
 
0.4%
ValueCountFrequency (%)
15 6
 
0.3%
13 3
 
0.1%
12 3
 
0.1%
11 4
 
0.2%
10 4
 
0.2%
9 8
 
0.4%
8 14
 
0.7%
7 37
1.8%
6 56
2.8%
5 83
4.1%

Web_Orders
Real number (ℝ)

High correlation  Zeros 

Distinct14
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.109623
Minimum0
Maximum27
Zeros47
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2025-07-14T20:50:24.388299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q36
95-th percentile9
Maximum27
Range27
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.7860672
Coefficient of variation (CV)0.67793741
Kurtosis5.3179225
Mean4.109623
Median Absolute Deviation (MAD)2
Skewness1.32232
Sum8285
Variance7.7621704
MonotonicityNot monotonic
2025-07-14T20:50:24.508005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2 342
17.0%
1 309
15.3%
3 295
14.6%
4 251
12.5%
5 197
9.8%
6 192
9.5%
7 134
 
6.6%
8 90
 
4.5%
9 73
 
3.6%
0 47
 
2.3%
Other values (4) 86
 
4.3%
ValueCountFrequency (%)
0 47
 
2.3%
1 309
15.3%
2 342
17.0%
3 295
14.6%
4 251
12.5%
5 197
9.8%
6 192
9.5%
7 134
 
6.6%
8 90
 
4.5%
9 73
 
3.6%
ValueCountFrequency (%)
27 2
 
0.1%
25 1
 
< 0.1%
11 42
 
2.1%
10 41
 
2.0%
9 73
 
3.6%
8 90
 
4.5%
7 134
6.6%
6 192
9.5%
5 197
9.8%
4 251
12.5%

Catalog_Orders
Real number (ℝ)

High correlation  Zeros 

Distinct14
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6612103
Minimum0
Maximum28
Zeros524
Zeros (%)26.0%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2025-07-14T20:50:24.620465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile9
Maximum28
Range28
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.8900107
Coefficient of variation (CV)1.0859761
Kurtosis6.6558076
Mean2.6612103
Median Absolute Deviation (MAD)2
Skewness1.7438438
Sum5365
Variance8.3521621
MonotonicityNot monotonic
2025-07-14T20:50:24.732265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 524
26.0%
1 445
22.1%
2 250
12.4%
3 170
 
8.4%
4 166
 
8.2%
5 128
 
6.3%
6 111
 
5.5%
7 72
 
3.6%
10 48
 
2.4%
8 47
 
2.3%
Other values (4) 55
 
2.7%
ValueCountFrequency (%)
0 524
26.0%
1 445
22.1%
2 250
12.4%
3 170
 
8.4%
4 166
 
8.2%
5 128
 
6.3%
6 111
 
5.5%
7 72
 
3.6%
8 47
 
2.3%
9 35
 
1.7%
ValueCountFrequency (%)
28 2
 
0.1%
22 1
 
< 0.1%
11 17
 
0.8%
10 48
 
2.4%
9 35
 
1.7%
8 47
 
2.3%
7 72
3.6%
6 111
5.5%
5 128
6.3%
4 166
8.2%

Store_Orders
Real number (ℝ)

High correlation 

Distinct14
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8100198
Minimum0
Maximum13
Zeros14
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2025-07-14T20:50:25.090046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median5
Q38
95-th percentile12
Maximum13
Range13
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.2456298
Coefficient of variation (CV)0.55862629
Kurtosis-0.6539452
Mean5.8100198
Median Absolute Deviation (MAD)2
Skewness0.68171573
Sum11713
Variance10.534113
MonotonicityNot monotonic
2025-07-14T20:50:25.422092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3 441
21.9%
4 287
14.2%
5 196
9.7%
2 196
9.7%
6 155
 
7.7%
8 142
 
7.0%
7 123
 
6.1%
10 121
 
6.0%
9 98
 
4.9%
12 94
 
4.7%
Other values (4) 163
 
8.1%
ValueCountFrequency (%)
0 14
 
0.7%
1 6
 
0.3%
2 196
9.7%
3 441
21.9%
4 287
14.2%
5 196
9.7%
6 155
 
7.7%
7 123
 
6.1%
8 142
 
7.0%
9 98
 
4.9%
ValueCountFrequency (%)
13 72
 
3.6%
12 94
 
4.7%
11 71
 
3.5%
10 121
6.0%
9 98
 
4.9%
8 142
7.0%
7 123
6.1%
6 155
7.7%
5 196
9.7%
4 287
14.2%

Web_Visits
Real number (ℝ)

High correlation 

Distinct16
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3209325
Minimum0
Maximum20
Zeros8
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size15.9 KiB
2025-07-14T20:50:25.613785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median6
Q37
95-th percentile8
Maximum20
Range20
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.438052
Coefficient of variation (CV)0.45820013
Kurtosis2.0652887
Mean5.3209325
Median Absolute Deviation (MAD)2
Skewness0.25889462
Sum10727
Variance5.9440976
MonotonicityNot monotonic
2025-07-14T20:50:25.801252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
7 350
17.4%
8 312
15.5%
6 310
15.4%
5 252
12.5%
4 198
9.8%
2 181
9.0%
3 180
8.9%
1 142
7.0%
9 71
 
3.5%
0 8
 
0.4%
Other values (6) 12
 
0.6%
ValueCountFrequency (%)
0 8
 
0.4%
1 142
7.0%
2 181
9.0%
3 180
8.9%
4 198
9.8%
5 252
12.5%
6 310
15.4%
7 350
17.4%
8 312
15.5%
9 71
 
3.5%
ValueCountFrequency (%)
20 3
 
0.1%
19 2
 
0.1%
17 1
 
< 0.1%
14 2
 
0.1%
13 1
 
< 0.1%
10 3
 
0.1%
9 71
 
3.5%
8 312
15.5%
7 350
17.4%
6 310
15.4%

Campaign_1
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size98.6 KiB
0
1872 
1
 
144

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2016
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 1872
92.9%
1 144
 
7.1%

Length

2025-07-14T20:50:26.038606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-14T20:50:26.164543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1872
92.9%
1 144
 
7.1%

Most occurring characters

ValueCountFrequency (%)
0 1872
92.9%
1 144
 
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2016
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1872
92.9%
1 144
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2016
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1872
92.9%
1 144
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2016
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1872
92.9%
1 144
 
7.1%

Campaign_2
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size98.6 KiB
0
1866 
1
 
150

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2016
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1866
92.6%
1 150
 
7.4%

Length

2025-07-14T20:50:26.290922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-14T20:50:26.385809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1866
92.6%
1 150
 
7.4%

Most occurring characters

ValueCountFrequency (%)
0 1866
92.6%
1 150
 
7.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2016
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1866
92.6%
1 150
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2016
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1866
92.6%
1 150
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2016
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1866
92.6%
1 150
 
7.4%

Campaign_3
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size98.6 KiB
0
1871 
1
 
145

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2016
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 1871
92.8%
1 145
 
7.2%

Length

2025-07-14T20:50:26.512615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-14T20:50:26.607754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1871
92.8%
1 145
 
7.2%

Most occurring characters

ValueCountFrequency (%)
0 1871
92.8%
1 145
 
7.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2016
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1871
92.8%
1 145
 
7.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2016
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1871
92.8%
1 145
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2016
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1871
92.8%
1 145
 
7.2%

Campaign_4
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size98.6 KiB
0
1887 
1
 
129

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2016
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 1887
93.6%
1 129
 
6.4%

Length

2025-07-14T20:50:26.745477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-14T20:50:26.837734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1887
93.6%
1 129
 
6.4%

Most occurring characters

ValueCountFrequency (%)
0 1887
93.6%
1 129
 
6.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2016
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1887
93.6%
1 129
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2016
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1887
93.6%
1 129
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2016
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1887
93.6%
1 129
 
6.4%

Campaign_5
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size98.6 KiB
0
1989 
1
 
27

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2016
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1989
98.7%
1 27
 
1.3%

Length

2025-07-14T20:50:26.995953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-14T20:50:27.107552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1989
98.7%
1 27
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 1989
98.7%
1 27
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2016
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1989
98.7%
1 27
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2016
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1989
98.7%
1 27
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2016
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1989
98.7%
1 27
 
1.3%

Complaint_Flag
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size98.6 KiB
0
1997 
1
 
19

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2016
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1997
99.1%
1 19
 
0.9%

Length

2025-07-14T20:50:27.218467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-14T20:50:27.308410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1997
99.1%
1 19
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 1997
99.1%
1 19
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2016
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1997
99.1%
1 19
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2016
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1997
99.1%
1 19
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2016
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1997
99.1%
1 19
 
0.9%

Contact_Cost
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size98.6 KiB
3
2016 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2016
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 2016
100.0%

Length

2025-07-14T20:50:27.694794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-14T20:50:27.774923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 2016
100.0%

Most occurring characters

ValueCountFrequency (%)
3 2016
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2016
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 2016
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2016
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 2016
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2016
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 2016
100.0%

Total_Revenue
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size100.5 KiB
11
2016 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters4032
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11
2nd row11
3rd row11
4th row11
5th row11

Common Values

ValueCountFrequency (%)
11 2016
100.0%

Length

2025-07-14T20:50:27.870135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-14T20:50:27.981445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
11 2016
100.0%

Most occurring characters

ValueCountFrequency (%)
1 4032
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4032
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 4032
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4032
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 4032
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4032
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 4032
100.0%

Next_Purchase
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size98.6 KiB
0
1719 
1
297 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2016
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1719
85.3%
1 297
 
14.7%

Length

2025-07-14T20:50:28.093418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-14T20:50:28.173219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1719
85.3%
1 297
 
14.7%

Most occurring characters

ValueCountFrequency (%)
0 1719
85.3%
1 297
 
14.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2016
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1719
85.3%
1 297
 
14.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2016
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1719
85.3%
1 297
 
14.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2016
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1719
85.3%
1 297
 
14.7%

Interactions

2025-07-14T20:50:08.989033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:06.253880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:12.050292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:16.213773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:20.554749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:24.656215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:28.728024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:32.964485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:37.038235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:41.175002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:46.592220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:52.211550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:57.116126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:50:00.995078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:50:04.930192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:50:09.243191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:06.635055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:12.355607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:16.467544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:20.764482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:24.890235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:29.023131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:33.327307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:37.325834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:41.440725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:46.977551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:52.559537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:57.436552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:50:01.231745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:50:05.145785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:50:09.491411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:06.898796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:12.662670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:16.727923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:20.980829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:25.141794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:29.274105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:33.569267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:37.572904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:41.790258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:47.350925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:52.937632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:57.683414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:50:01.462554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:50:05.374600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:50:09.808223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:07.168781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:13.036170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:16.993697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:21.344700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:25.402724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:29.538684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:33.810443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:37.790331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:42.163545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:47.731870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:53.315887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:57.910346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:50:01.730181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:50:05.696043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:50:10.150793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:07.395446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:13.274198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:17.296255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:21.769588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:25.649553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:29.781000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:34.038700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:38.008521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:42.503999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:48.073655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:53.625759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:58.129654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:50:01.963156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:50:05.964802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:50:10.419699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:07.699256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:13.539762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:17.657279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:22.039580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:25.933589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:30.008106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:34.293068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:38.245781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:42.792431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:48.414274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:53.939864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:58.339821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:50:02.293243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:50:06.213472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:50:10.684614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:08.079968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:13.804229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:17.931650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:22.277216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:26.263615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:30.387781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:34.677709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:38.562703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:43.369265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:48.717913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:54.211078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:58.553821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:50:02.608111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:50:06.451108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:50:10.950312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:08.323884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:14.038506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:18.181459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:22.493938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:26.507992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:30.637271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:34.911103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:38.855910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:43.693087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:49.053798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:54.513461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:58.809301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:50:02.929536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:50:06.699719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:50:11.329147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:08.583988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:14.373298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:18.430843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:22.835433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:26.755621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:30.871641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:35.143614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:39.080795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:43.997792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:49.344387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:54.809237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:59.073026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:50:03.201071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:50:06.954687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:50:11.607348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:08.812247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:14.679505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:18.722801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:23.130636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:26.999623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:31.121543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:35.371033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:39.281440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:44.303015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:49.982826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:55.090398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:59.365597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:50:03.419083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:50:07.269843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:50:11.866613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:10.486914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:14.890589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:19.017214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:23.381975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:27.298429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:31.408327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:35.638685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:39.613862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:44.675408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:50.453554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:55.362985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:59.585463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:50:03.683670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:50:07.696253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:50:12.123406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:10.808444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:15.146413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:19.351500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:23.623803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:27.632697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:31.761313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:35.914463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:39.877545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:45.128680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:50.821378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:55.638018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:59.880778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:50:03.899084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:50:07.950048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:50:12.385297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:11.100487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:15.377394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:19.673257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:23.884186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:27.859737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:32.206767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:36.161229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:40.242676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:45.478733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:51.183069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:55.962389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:50:00.141311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:50:04.170871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:50:08.180206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:50:12.716241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:11.385879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:15.605290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:20.004680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:24.082572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:28.172211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:32.453497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:36.448788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:40.562841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:45.833824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:51.533982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:56.210844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:50:00.425338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:50:04.466827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:50:08.458760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:50:13.015502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:11.693135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:15.969584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:20.280021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:24.291306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:28.400991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:32.702608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:36.734051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:40.832508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:46.193275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:51.859747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:49:56.524004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:50:00.700779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:50:04.676505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-14T20:50:08.736311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-07-14T20:50:28.284838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Annual_IncomeBirth_YearCampaign_1Campaign_2Campaign_3Campaign_4Campaign_5Catalog_OrdersComplaint_FlagEdu_LevelFamily_StatusKids_CountLast_VisitNext_PurchasePromo_PurchasesSpent_FishSpent_FruitsSpent_GoldSpent_MeatSpent_SweetsSpent_WinesStore_OrdersTeens_CountUser_KeyWeb_OrdersWeb_Visits
Annual_Income1.000-0.2200.0000.1420.4450.3640.0610.7930.0000.0530.0000.2840.0190.192-0.2070.5750.5790.5000.8190.5680.8270.7270.178-0.0030.569-0.645
Birth_Year-0.2201.0000.0450.0390.0820.0360.018-0.1750.1450.1160.0910.218-0.0310.000-0.082-0.029-0.021-0.079-0.1140.007-0.232-0.1650.3130.008-0.1680.132
Campaign_10.0000.0451.0000.0720.0720.0860.0560.0990.0000.0000.0000.0130.0330.2510.0000.0920.0450.1040.0310.0000.0940.1700.0340.0000.0410.075
Campaign_20.1420.0390.0721.0000.3190.2530.2870.2040.0000.0370.0000.1540.0000.1820.0170.0390.0800.0510.1040.0430.3950.2030.0260.0000.1570.000
Campaign_30.4450.0820.0720.3191.0000.3860.2250.3470.0000.0280.0260.2030.0000.3200.2370.2520.2790.1700.3770.2550.5270.2250.1970.0000.1680.306
Campaign_40.3640.0360.0860.2530.3861.0000.1530.3080.0000.0350.0360.1760.0000.2710.1630.2630.2620.1480.3060.2580.3550.1970.1380.0610.1680.201
Campaign_50.0610.0180.0560.2870.2250.1531.0000.1080.0000.0260.0000.0750.0360.1630.0000.0620.0000.0490.0000.0330.3110.0820.0000.0350.0170.000
Catalog_Orders0.793-0.1750.0990.2040.3470.3080.1081.0000.0000.0660.0000.3860.0480.210-0.0450.6530.6360.6480.8520.6290.8240.7110.114-0.0260.622-0.528
Complaint_Flag0.0000.1450.0000.0000.0000.0000.0000.0001.0000.0270.0000.0310.0000.0000.0000.0000.0000.0000.0000.0000.0000.0070.0000.0000.0070.000
Edu_Level0.0530.1160.0000.0370.0280.0350.0260.0660.0271.0000.0000.0670.0000.0840.0000.0600.0640.0620.0540.0610.1110.1040.1060.0000.0840.053
Family_Status0.0000.0910.0000.0000.0260.0360.0000.0000.0000.0001.0000.0430.0200.1450.0160.0540.0300.0580.0180.0000.0200.0250.0760.0000.0470.000
Kids_Count0.2840.2180.0130.1540.2030.1760.0750.3860.0310.0670.0431.0000.0770.0720.2200.3160.3110.2650.3220.2880.4010.3990.0570.0000.2930.341
Last_Visit0.019-0.0310.0330.0000.0000.0000.0360.0480.0000.0000.0200.0771.0000.1950.0030.0190.0200.0190.0380.0230.0330.0100.047-0.0420.002-0.025
Next_Purchase0.1920.0000.2510.1820.3200.2710.1630.2100.0000.0840.1450.0720.1951.0000.1110.1280.1490.1340.2320.1170.2730.1460.1520.0170.1730.125
Promo_Purchases-0.207-0.0820.0000.0170.2370.1630.000-0.0450.0000.0000.0160.2200.0030.1111.000-0.124-0.1090.087-0.037-0.1140.0560.0980.343-0.0240.2920.409
Spent_Fish0.575-0.0290.0920.0390.2520.2630.0620.6530.0000.0600.0540.3160.0190.128-0.1241.0000.7070.5630.7290.7050.5190.5820.140-0.0410.464-0.454
Spent_Fruits0.579-0.0210.0450.0800.2790.2620.0000.6360.0000.0640.0300.3110.0200.149-0.1090.7071.0000.5640.7170.6930.5200.5840.117-0.0180.470-0.439
Spent_Gold0.500-0.0790.1040.0510.1700.1480.0490.6480.0000.0620.0580.2650.0190.1340.0870.5630.5641.0000.6380.5420.5730.5400.000-0.0440.578-0.250
Spent_Meat0.819-0.1140.0310.1040.3770.3060.0000.8520.0000.0540.0180.3220.0380.232-0.0370.7290.7170.6381.0000.7010.8250.7830.228-0.0210.678-0.488
Spent_Sweets0.5680.0070.0000.0430.2550.2580.0330.6290.0000.0610.0000.2880.0230.117-0.1140.7050.6930.5420.7011.0000.5070.5820.099-0.0400.463-0.450
Spent_Wines0.827-0.2320.0940.3950.5270.3550.3110.8240.0000.1110.0200.4010.0330.2730.0560.5190.5200.5730.8250.5071.0000.8050.115-0.0350.740-0.383
Store_Orders0.727-0.1650.1700.2030.2250.1970.0820.7110.0070.1040.0250.3990.0100.1460.0980.5820.5840.5400.7830.5820.8051.0000.085-0.0330.678-0.450
Teens_Count0.1780.3130.0340.0260.1970.1380.0000.1140.0000.1060.0760.0570.0470.1520.3430.1400.1170.0000.2280.0990.1150.0851.0000.0000.1330.224
User_Key-0.0030.0080.0000.0000.0000.0610.035-0.0260.0000.0000.0000.000-0.0420.017-0.024-0.041-0.018-0.044-0.021-0.040-0.035-0.0330.0001.000-0.032-0.001
Web_Orders0.569-0.1680.0410.1570.1680.1680.0170.6220.0070.0840.0470.2930.0020.1730.2920.4640.4700.5780.6780.4630.7400.6780.133-0.0321.000-0.087
Web_Visits-0.6450.1320.0750.0000.3060.2010.000-0.5280.0000.0530.0000.341-0.0250.1250.409-0.454-0.439-0.250-0.488-0.450-0.383-0.4500.224-0.001-0.0871.000

Missing values

2025-07-14T20:50:13.460750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-14T20:50:14.027043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

User_KeyBirth_YearEdu_LevelFamily_StatusAnnual_IncomeKids_CountTeens_CountReg_DateLast_VisitSpent_WinesSpent_FruitsSpent_MeatSpent_FishSpent_SweetsSpent_GoldPromo_PurchasesWeb_OrdersCatalog_OrdersStore_OrdersWeb_VisitsCampaign_1Campaign_2Campaign_3Campaign_4Campaign_5Complaint_FlagContact_CostTotal_RevenueNext_Purchase
026071953GraduationSingle40464.00111-01-2013784241711872341682880000003110
172471960GraduationWidow47916.00122-11-2012725050260075574660100003110
258021972BasicMarried14188.00028-02-2013402711161227120460000003110
321471969GraduationTogether76653.00016-08-201391736639462191891261471120011003110
437591958GraduationTogether65196.00225-07-201334743191811202002761151000003110
592841958GraduationTogether53977.00108-06-20132162016165024825551250000003110
6105051960MasterTogether73113.00026-12-2013867411915450928134720000003110
725791957GraduationMarried71113.00117-12-2013954953325511338467940000003110
8107791983GraduationSingle22148.00013-04-201416155140411110370000003110
980791982GraduationMarried22448.01026-02-20148631823218321330000003110
User_KeyBirth_YearEdu_LevelFamily_StatusAnnual_IncomeKids_CountTeens_CountReg_DateLast_VisitSpent_WinesSpent_FruitsSpent_MeatSpent_FishSpent_SweetsSpent_GoldPromo_PurchasesWeb_OrdersCatalog_OrdersStore_OrdersWeb_VisitsCampaign_1Campaign_2Campaign_3Campaign_4Campaign_5Complaint_FlagContact_CostTotal_RevenueNext_Purchase
200610481972MasterMarried35641.01014-07-201311633678928231470000003111
200776791985PhDSingle30298.00019-05-2014486312610111330000003110
2008779819722n CycleTogether46344.00114-12-201228233205782091471570000003110
2009464619512n CycleMarried78497.00001-12-201344207264477502231571220001003110
20108719812n CycleMarried27733.01026-08-20131607526217220370000003110
201189391959GraduationDivorced61250.00116-12-201249382138656026596521250000003110
2012761319742n CycleTogether49669.01024-05-201497166510711829251660000003110
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